In-process multiscale performance evaluation of FDM-based honeycomb structures with geometric defects

Abstract

Material extrusion additive manufacturing has been extensively used in fabricating structures with complex geometries. However, geometric defects often exist during the processing that decreases the final performance of printed structures. In this paper, a real-time multiscale performance evaluation method is developed for material extrusion-based honeycomb structures. The representative cell boundary is extracted from 3D point clouds obtained via an in-situ monitoring system. The cell boundary is then used to generate the digital twin of the unit cell of the printed layer based on the finite element (FE) method. A physics-based multiscale modeling approach called mechanics of structure genome (MSG) is then employed to predict the effective material properties of the printed layer and plate stiffness matrix of the final structure. The proposed approach provides a highly efficient way to predict the real-time performance of the as-manufactured products. Moreover, the numerical example shows that the geometric defects may increase the stiffness but decrease the strength of the structure, which cannot be captured by the conventional in-situ monitoring approaches. To further improve the computational efficiency, a convolutional neural networks (CNN) model is developed to predict the effective material properties based on the in-situ scanned data.